• Genomics & Informatics
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• 제8권3호
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• pp.142-149
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• 2010

In recent years, genome-wide association (GWA) studies have successfully led to many discoveries of genetic variants affecting common complex traits, including height, blood pressure, and diabetes. Although GWA studies have made much progress in finding single nucleotide polymorphisms (SNPs) associated with many complex traits, such SNPs have been shown to explain only a very small proportion of the underlying genetic variance of complex traits. This is partly due to that fact that most current GWA studies have relied on single-marker approaches that identify single genetic factors individually and have limitations in considering the joint effects of multiple genetic factors on complex traits. Joint identification of multiple genetic factors would be more powerful and provide a better prediction of complex traits, since it utilizes combined information across variants. Recently, a new statistical method for joint identification of genetic variants for common complex traits via the elastic-net regularization method was proposed. In this study, we applied this joint identification approach to a large-scale GWA dataset (i.e., 8842 samples and 327,872 SNPs) in order to identify genetic variants of obesity for the Korean population. In addition, in order to test for the biological significance of the jointly identified SNPs, gene ontology and pathway enrichment analyses were further conducted.

• Genomics & Informatics
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• 제12권3호
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• pp.121-126
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• 2014

Medication adherence is generally defined as the extent of voluntary cooperation of a patient in taking medicine as prescribed. Adherence to long-term treatment with chronic disease is essential for reducing disease comorbidity and mortality. However, medication non-adherence in chronic disease averages 50%. This study was conducted a genome-wide association study to identify the genetic basis of medication adherence. A total of 235 medication non-adherents and 1,067 medication adherents with hypertension or diabetes were used from the Korean Association Resource project data according to the self-reported treatment status of each chronic disease, respectively. We identified four single nucleotide polymorphisms with suggestive genome-wide association. The most significant single nucleotide polymorphism was rs6978712 (chromosome 7, $p=4.87{\times}10^{-7}$), which is located proximal to the GCC1 gene, which was previously implicated in decision-making capability in drug abusers. Two suggestive single nucleotide polymorphisms were in strong linkage disequilibrium ($r^2$ > 0.8) with rs6978712. Thus, in the aspect of decision-making in adherence behavior, the association between medication adherence and three loci proximal to the GCC1 gene seems worthy of further research. However, to overcome a few limitations in this study, defining the standardized phenotype criteria for self-reported adherence should be performed before replicating association studies.

• Molecules and Cells
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• 제37권1호
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• pp.36-42
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• 2014

The tomato (Solanum lycopersicum L.) is a model plant for genome research in Solanaceae, as well as for studying crop breeding. Genome-wide single nucleotide polymorphisms (SNPs) are a valuable resource in genetic research and breeding. However, to do discovery of genome-wide SNPs, most methods require expensive high-depth sequencing. Here, we describe a method for SNP calling using a modified version of SAMtools that improved its sensitivity. We analyzed 90 Gb of raw sequence data from next-generation sequencing of two resequencing and seven transcriptome data sets from several tomato accessions. Our study identified 4,812,432 non-redundant SNPs. Moreover, the workflow of SNP calling was improved by aligning the reference genome with its own raw data. Using this approach, 131,785 SNPs were discovered from transcriptome data of seven accessions. In addition, 4,680,647 SNPs were identified from the genome of S. pimpinellifolium, which are 60 times more than 71,637 of the PI212816 transcriptome. SNP distribution was compared between the whole genome and transcriptome of S. pimpinellifolium. Moreover, we surveyed the location of SNPs within genic and intergenic regions. Our results indicated that the sufficient genome-wide SNP markers and very sensitive SNP calling method allow for application of marker assisted breeding and genome-wide association studies.

• Genomics & Informatics
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• 제8권3호
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• pp.122-130
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• 2010

Abnormal hematological values are associated with various disorders including cancer and cardiovascular, metabolic, infectious, and immune diseases. We report the copy number variations (CNVs) in clinically relevant hematological parameters, including hemoglobin level, red and white blood cell counts, platelet counts, and red blood cell (RBC) volume. We describe CNVs in several loci associated with these hematological parameters in 8,842 samples from Korean population-based studies. The data that we evaluated included four RBC parameters, one platelet parameter, and one associated with total white blood cell (WBC) count, exceeding the genome-wide significance. We show that CNVs in hematological parameters are associated with some loci, different from previously associated loci reported in single nucleotide polymorphism (SNP) association studies.

• Asian-Australasian Journal of Animal Sciences
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• 제30권3호
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• pp.309-319
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• 2017

Objective: Holsteins are known as the world's highest-milk producing dairy cattle. The purpose of this study was to identify genetic regions strongly associated with milk traits (milk production, fat, and protein) using Korean Holstein data. Methods: This study was performed using single nucleotide polymorphism (SNP) chip data (Illumina BovineSNP50 Beadchip) of 911 Korean Holstein individuals. We inferred each genomic estimated breeding values based on best linear unbiased prediction (BLUP) and ridge regression using BLUPF90 and R. We then performed a genome-wide association study and identified genetic regions related to milk traits. Results: We identified 9, 6, and 17 significant genetic regions related to milk production, fat and protein, respectively. These genes are newly reported in the genetic association with milk traits of Holstein. Conclusion: This study complements a recent Holstein genome-wide association studies that identified other SNPs and genes as the most significant variants. These results will help to expand the knowledge of the polygenic nature of milk production in Holsteins.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• 제29권2호
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• pp.62-72
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• 2018

Objectives: The molecular mechanisms underlying attention-deficit hyperactivity disorder (ADHD) remain unclear. Therefore, this study aimed to identify the genetic susceptibility loci for ADHD in Korean children with ADHD. We performed a case-control and a family-based genome-wide association study (GWAS), as well as genome-wide quantitative trait locus (QTL) analyses, for two symptom traits. Methods: A total of 135 subjects (71 cases and 64 controls), for the case-control analysis, and 54 subjects (27 probands and 27 unaffected siblings), for the family-based analysis, were included. Results: The genome-wide QTL analysis identified four single nucleotide polymorphisms (SNPs) (rs7684645 near APELA, rs12538843 near YAE1D1 and POU6F2, rs11074258 near MCTP2, and rs34396552 near CIDEA) that were significantly associated with the number of inattention symptoms in ADHD. These SNPs showed possible association with ADHD in the family-based GWAS, and with hyperactivity-impulsivity in genome-wide QTL analyses. Moreover, association signals in the family-based QTL analysis for the number of inattention symptoms were clustered near genes IL10, IL19, SCL5A9, and SKINTL. Conclusion: We have identified four QTLs with genome-wide significance and several promising candidates that could potentially be associated with ADHD (CXCR4, UPF1, SETD5, NALCN-AS1, ERC1, SOX2-OT, FGFR2, ANO4, and TBL1XR1). Further replication studies with larger sample sizes are needed.

• Genomics & Informatics
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• 제11권3호
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• pp.135-141
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• 2013

Gene set analysis is a powerful tool for interpreting a genome-wide association study result and is gaining popularity these days. Comparison of the gene sets obtained for a variety of traits measured from a single genetic epidemiology dataset may give insights into the biological mechanisms underlying these traits. Based on the previously published single nucleotide polymorphism (SNP) genotype data on 8,842 individuals enrolled in the Korea Association Resource project, we performed a series of systematic genome-wide association analyses for 49 quantitative traits of basic epidemiological, anthropometric, or blood chemistry parameters. Each analysis result was subjected to subsequent gene set analyses based on Gene Ontology (GO) terms using gene set analysis software, GSA-SNP, identifying a set of GO terms significantly associated to each trait ($p_{corr}$ < 0.05). Pairwise comparison of the traits in terms of the semantic similarity in their GO sets revealed surprising cases where phenotypically uncorrelated traits showed high similarity in terms of biological pathways. For example, the pH level was related to 7 other traits that showed low phenotypic correlations with it. A literature survey implies that these traits may be regulated partly by common pathways that involve neuronal or nerve systems.

• Genomics & Informatics
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• 제12권4호
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• pp.216-224
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• 2014

Gastritis is a common but a serious disease with a potential risk of developing carcinoma. Helicobacter pylori infection is reported as the most common cause of gastritis, but other genetic and genomic factors exist, especially single-nucleotide polymorphisms (SNPs). Association studies between SNPs and gastritis disease are important, but results on epistatic interactions from multiple SNPs are rarely found in previous genome-wide association (GWA) studies. In this study, we performed computational GWA case-control studies for gastritis in Korea Associated Resource (KARE) data. By transforming the resulting SNP epistasis network into a gene-gene epistasis network, we also identified potential gene-gene interaction factors that affect the susceptibility to gastritis.

• Genomics & Informatics
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• 제14권4호
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• pp.160-165
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• 2016

Over the past decade, the detection of gene-gene interactions has become more and more popular in the field of genome-wide association studies (GWASs). The goal of the GWAS is to identify genetic susceptibility to complex diseases by assaying and analyzing hundreds of thousands of single-nucleotide polymorphisms. However, such tests are computationally demanding and methodologically challenging. Recently, a simple but powerful method, named "BOolean Operation-based Screening and Testing" (BOOST), was proposed for genome-wide gene-gene interaction analyses. BOOST was designed with a Boolean representation of genotype data and is approximately equivalent to the log-linear model. It is extremely fast, and genome-wide gene-gene interaction analyses can be completed within a few hours. However, BOOST can not adjust for covariate effects, and its type-1 error control is not correct. Thus, we considered two-step approaches for gene-gene interaction analyses. First, we selected gene-gene interactions with BOOST and applied logistic regression with covariate adjustments to select gene-gene interactions. We applied the two-step approach to type 2 diabetes (T2D) in the Korea Association Resource (KARE) cohort and identified some promising pairs of single-nucleotide polymorphisms associated with T2D.

• Genomics & Informatics
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• 제20권1호
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• pp.14.1-14.4
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• 2022

Permutation testing is a robust and popular approach for significance testing in genomic research that has the advantage of reducing inflated type 1 error rates; however, its computational cost is notorious in genome-wide association studies (GWAS). Here, we developed a supercomputing-aided approach to accelerate the permutation testing for GWAS, based on the message-passing interface (MPI) on parallel computing architecture. Our application, called MPI-GWAS, conducts MPI-based permutation testing using a parallel computing approach with our supercomputing system, Nurion (8,305 compute nodes, and 563,740 central processing units [CPUs]). For 10⁷ permutations of one locus in MPI-GWAS, it was calculated in 600 s using 2,720 CPU cores. For 10⁷ permutations of ~30,000-50,000 loci in over 7,000 subjects, the total elapsed time was ~4 days in the Nurion supercomputer. Thus, MPI-GWAS enables us to feasibly compute the permutation-based GWAS within a reason-able time by harnessing the power of parallel computing resources.